Advances in Artificial Intelligence are challenged by the biases rooted in the datasets used to train the models. In image geolocation estimation, models are mostly trained using data from specific geographic regions, notably the Western world, and as a result, they may struggle to comprehend the complexities of underrepresented regions. To assess this issue, we apply a state-of-the-art image geolocation estimation model (ISNs) to a crowd-sourced dataset of geolocated images from the African continent (SCA100), and then explore the regional and socioeconomic biases underlying the model's predictions. Our findings show that the ISNs model tends to over-predict image locations in high-income countries of the Western world, which is consistent with the geographic distribution of its training data, i.e., the IM2GPS3k dataset. Accordingly, when compared to the IM2GPS3k benchmark, the accuracy of the ISNs model notably decreases at all scales. Additionally, we cluster images of the SCA100 dataset based on how accurately they are predicted by the ISNs model and show the model's difficulties in correctly predicting the locations of images in low income regions, especially in Sub-Saharan Africa. Therefore, our results suggest that using IM2GPS3k as a training set and benchmark for image geolocation estimation and other computer vision models overlooks its potential application in the African context.

翻译：人工智能的进步受到训练模型所用数据集固有偏差的挑战。在图像地理位置估计中，模型主要使用特定地理区域（尤其是西方世界）的数据进行训练，因此可能难以理解代表性不足区域的复杂性。为评估这一问题，我们将最先进的图像地理位置估计模型（ISNs）应用于来自非洲大陆的众包地理标注图像数据集（SCA100），进而探究模型预测背后的区域和社会经济偏差。研究结果表明，ISNs模型倾向于过度预测西方世界高收入国家的图像位置，这与其训练数据（即IM2GPS3k数据集）的地理分布一致。相应地，与IM2GPS3k基准相比，ISNs模型在所有尺度上的准确率均显著下降。此外，我们根据ISNs模型预测的准确度对SCA100数据集的图像进行聚类分析，发现该模型在低收入地区（尤其是撒哈拉以南非洲）的图像位置预测中存在显著困难。因此，我们的研究结果表明，将IM2GPS3k作为图像地理位置估计及其他计算机视觉模型的训练集和基准，忽视了其在非洲背景下的潜在应用价值。